Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Pagerank for product image search
Proceedings of the 17th international conference on World Wide Web
NUS-WIDE: a real-world web image database from National University of Singapore
Proceedings of the ACM International Conference on Image and Video Retrieval
Diversity ranking for video retrieval from a broadcaster archive
Proceedings of the 1st ACM International Conference on Multimedia Retrieval
Knowledge propagation in large image databases using neighborhood information
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Improving image tags by exploiting web search results
Multimedia Tools and Applications
Annotation propagation in image databases using similarity graphs
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Ranking in heterogeneous social media
Proceedings of the 7th ACM international conference on Web search and data mining
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With the explosive growth of digital cameras and online media, it has become crucial to design efficient methods that help users browse and search large image collections. The recent VisualRank algorithm [4] employs visual similarity to represent the link structure in a graph so that the classic PageRank algorithm can be applied to select the most relevant images. However, measuring visual similarity is difficult when there exist diversified semantics in the image collection, and the results from VisualRank cannot supply good visual summarization with diversity. This paper proposes to rank the images in a structural fashion, which aims to discover the diverse structure embedded in photo collections, and rank the images according to their similarity among local neighborhoods instead of across the entire photo collection. We design a novel algorithm named RankCompete, which generalizes the PageRank algorithm for the task of simultaneous ranking and clustering. The experimental results show that RankCompete outperforms VisualRank and provides an efficient but effective tool for organizing web photos.